Validity of Apgar Score as an Indicator of Neonatal SARS-CoV-2 Infection: A Scoping Review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has had profound impacts on healthcare systems worldwide, particularly regarding the care of pregnant women and their neonates. The use of the Apgar score—a discrete numerical index used to evaluate neonatal condition immediately following delivery that has been used ubiquitously as a clinical indicator of neonatal condition and widely reported in the literature for decades—has continued during the pandemic. Although health systems adopted protocols that addressed pregnant women and their neonates during the pandemic, limited research has assessed the validity of Apgar scores for determining neonatal conditions in the context of COVID-19. Therefore, this scoping review was conducted on the first 2 years of the pandemic and included mothers with reverse transcription-polymerase chain reaction confirmed COVID-19 and their resulting positive or negative neonates. In total, 1,966 articles were assessed for eligibility, yielding 246 articles describing 663 neonates. Neonates who tested negative had median Apgar scores of 9 and 9 at 1 and 5 mins, respectively, while test-positive neonates had median Apgar scores of 8 and 9 at the same time points. The proportions of test-negative neonates with Apgar scores below 7 were 29 (4%) and 11 (2%) at 1 and 5 mins, which was not statistically significant (p = 0.327, χ2 = 0.961). These proportions were even lower for positive neonates: 22 (3%) and 11 (2%) at 1 and 5 mins, respectively, which was not statistically significant (p = 1, χ2 = 0). The low proportion of Apgar scores below 7 suggests that low Apgar scores are likely to be associated with severe maternal COVID-19 symptoms during delivery rather than neonatal COVID-19. Therefore, this study indicated that Apgar scores are poor indicators of neonatal COVID-19 status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".